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# Staff AI Research Engineer - Computer Vision

- **Company:** Helsing GmbH
- **Location:** Berlin, Germany (Remote available)
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Computer Vision, C++ (Programming Language), Python (Programming Language), Machine Learning, Object Detection, Software Engineering, Deep Learning, Information Technology, GPT, Data Generation
- **Published:** November 7, 2026
- **Apply:** https://startup.jobs/staff-ai-research-engineer-computer-vision-helsing-10709770

## About the Role

* Hold an MSc in computer science, machine learning, robotics, or a closely related field, with experience in designing, implementing, and thoroughly evaluating advanced AI-based systems.
 * Have hands-on experience developing and deploying computer vision models. You have iterated on models beyond benchmarks and understand what it takes to make visual perception reliable under real-world data distributions and deployment constraints.
 * Are deeply familiar with modern deep learning architectures for visual perception, including convolutional and transformer-based models, and have applied techniques such as multi-task learning, domain adaptation, or model compression to practical problems.
 * Possess solid software engineering skills, writing clean and well-structured code in Python, and have experience deploying AI software to production including testing, QA, and monitoring.
 * Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internally and externally.
 * Are passionate about keeping up to date with current research and enjoy reimplementing and extending state-of-the-art approaches in computer vision and deep learning.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don't meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points., * PhD in computer vision, machine learning, robotics, or a related field, with publications in top-tier venues (e.g. CVPR, NeurIPS, ICLR, ICCV, ICRA, IROS).

 * Experience with object detection and classification, single and multi-object tracking, or scene understanding.
 * Experience designing, evaluating, and delivering end-to-end AI systems on edge devices with constrained compute resources.
 * Experience with simulators, emulators, or synthetic data generation pipelines.
 * Experience with Rust and/or C++.

## Description

At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and robustness against adversarial attacks.

You will be part of a computer vision team responsible for building models for object detection, recognition, classification, and tracking. You will develop and extend state-of-the-art architectures and pipelines, design rigorous experiments, and conduct benchmarks to evaluate and improve real-world performance, including adaptation to the compute constraints and operational requirements of downstream deployments. You will collaborate across research, engineering, and product teams on high-impact projects.

Typical examples of day-to-day responsibilities include, depending on the team:

 * Optimising models for real-time deployed performance on target hardware: selecting the best architecture or layers for throughput and latency constraints, curating and leveraging data to improve performance
 * Researching new model architectures, replicating and adapting SOTA detection, tracking, or classification methods to concrete use cases according to operational needs
 * Running multi-GPU or multi-node training jobs on large-scale sensor datasets to build powerful foundation or auto-annotation models
 * Interacting with product and downstream deployment teams to identify perception bottlenecks, prioritise improvements, and shape the roadmap for new capabilities